Systems and methods for determining energy information using an organizational model of an industrial automation system

ABSTRACT

A system may include a processor that may receive energy data associated with one or more assets in an automation system, receive organizational model data associated with the automation system, and generate one or more energy reports based on a relationship between the energy data and the organizational model data.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is related to U.S. patent application Ser. No.12/684,469, entitled “Industrial Control Energy Object,” filed Jan. 8,2010, which is herein incorporated by reference. This application isalso related to U.S. patent application Ser. No. 13/275,983, entitled“Industrial Control Energy Object,” filed Oct. 18, 2011, which is hereinincorporated by reference.

BACKGROUND

The present disclosure relates generally to collecting and organizingenergy information from assets being employed in an industrialautomation system. More particularly, embodiments of the presentdisclosure relate to using an organizational model in conjunction withenergy information acquired from assets in the industrial automationsystem to determine energy data (i.e., consumption or production) forindividual assets and for individual and scalable parts of theindustrial automation system.

Industrial automation systems generally include a variety of energyconsuming assets employed in a production process (e.g., differentassembly lines for a single product) or the like. Some of the assets inthe industrial automation system may be capable of communicating itscorresponding energy data with other controllers within the industrialautomation system or to a supervisory control system that may bestationed outside the industrial automation system. In any case,although the energy information acquired via the communicating assetsmay be beneficial in understanding how energy is being utilized withinthe industrial automation system, the acquired energy information isoften individualized such that it primarily provides information relatedto a specific device without regard to how the energy information isrelated to scalable parts of the industrial automation system or theindustrial automation system as a whole. Accordingly, improved systemsand methods for analyzing the energy information related to scalableparts of the industrial automation system are desirable.

BRIEF DESCRIPTION

In one embodiment, a system may include a processor that may receiveenergy data associated with one or more assets in an automation system,receive organizational model data associated with the automation system,and generate one or more energy reports based on a relationship betweenthe energy data and the organizational model data.

In another embodiment, a system may include a processor that may receiveenergy data associated with one or more assets in an automation systemand receive organizational model data associated with the automationsystem such that the organizational model data may include an assetenergy profile for at least one of the assets. The processor may thengenerate a list of missing assets that do not have an asset energyprofile in the organizational model data and generate one or more energyreports based on a relationship between a subset of the energy data andthe organizational model data, wherein the subset of the energy datacorrespond to the at least one of the assets.

In yet another embodiment, a system may include a processor that mayreceive energy data associated with one or more assets in an automationsystem and receive organizational model data associated with theautomation system such that the organizational model data may include anasset energy profile for at least one of the assets. The processor maythen retrieve one or more missing asset energy profiles that are not inthe organizational model data and generate one or more energy reportsbased on a relationship between a the energy data, the retrieved missingasset energy profiles and the organizational model data.

DRAWINGS

These and other features, aspects, and advantages of the presentinvention will become better understood when the following detaileddescription is read with reference to the accompanying drawings in whichlike characters represent like parts throughout the drawings, wherein:

FIG. 1 is a block diagram of an energy management system for anindustrial automation system, in accordance with an embodiment;

FIG. 2 is an example of a energy structure that may be used as an inputfor the energy management system of FIG. 1, in accordance with anembodiment;

FIG. 3 is an example of an organizational model that may be used as aninput for the energy management system of FIG. 1, in accordance with anembodiment;

FIG. 4 is an example of logical energy data that may be output by theenergy management system of FIG. 1, in accordance with an embodiment;

FIG. 5 is a flow chart of a method for categorizing energy data based onthe organizational model that may be used as an input for the energymanagement system of FIG. 1, in accordance with an embodiment;

FIG. 6 is a flow chart of a method for identifying missing energyprofiles for assets in the organizational model that may be used as aninput for the energy management system of FIG. 1, in accordance with anembodiment;

FIG. 7 is a flow chart of a method for retrieving energy profiles forassets missing energy profile data in the organizational model that maybe used as an input for the energy management system of FIG. 1, inaccordance with an embodiment;

FIG. 8 is a block diagram of an energy inference engine that may beemployed in the energy management system of FIG. 1, in accordance withan embodiment;

FIG. 9 is a block diagram of an energy state engine that may be employedin the energy management system of FIG. 1, in accordance with anembodiment;

FIG. 10 is a flow chart of a method for placing assets in the industrialautomation system into a reduced power consumption mode based on energyinformation determined by the energy inference engine of FIG. 8, theenergy state engine of FIG. 9, or both, in accordance with anembodiment;

FIG. 11 is a flow chart of a method for coordinating the use of assetsin the industrial automation system based on peak energy times andenergy information determined by the energy inference engine of FIG. 8,the energy state engine of FIG. 9, or both, in accordance with anembodiment;

FIG. 12 is a flow chart of a method for coordinating the use of assetsin the industrial automation system based on a utility demand scheduleand energy information determined by the energy inference engine of FIG.8, the energy state engine of FIG. 9, or both, in accordance with anembodiment;

FIG. 13 is a flow chart of a method for notifying an operator in theindustrial automation system when energy usage of a component fallsoutside an expected range based on energy information determined by theenergy inference engine of FIG. 8, the energy state engine of FIG. 9, orboth, in accordance with an embodiment;

FIG. 14 is a flow chart of a method for modifying a scheduled use ofassets in the industrial automation system based on a utility demandschedule and energy information determined by the energy inferenceengine of FIG. 8, the energy state engine of FIG. 9, or both, inaccordance with an embodiment; and

FIG. 15 is a block diagram of a multi-core processor that may beemployed in the energy management system of FIG. 1, in accordance withan embodiment.

DETAILED DESCRIPTION

The present disclosure is generally directed towards leveraging anorganizational model of an industrial automation system with energy dataacquired from various assets or devices in the industrial automationsystem to better understand the energy consumption or production of theassets and various scalable areas within the industrial automationsystem. Generally, energy information acquired from any asset in theindustrial automation system may not provide any details with regard tohow its energy information may relate to the industrial automationsystem. That is, the energy information typically does not provide acontext in which the energy information may be used with regard to theindustrial automation system as a whole. Instead, the energy informationis individualized with regard to a particular asset and may be used toknow the energy being consumed at specific points in the industrialautomation system; however, this information is not useful inunderstanding how the energy may be used more efficiently within theindustrial automation system. By leveraging the organizational modelwith the acquired energy information, the presently disclosed systemsand techniques may provide an industrial automation system-wideintegrated architecture that may enable different processes, areas, andassets in the industrial automation system to be used in the industrialautomation system-wide energy more efficiently.

By way of introduction, FIG. 1 depicts a block diagram of an energymanagement system 10 for an industrial automation system. Although thedisclosure is described with reference to an industrial automationsystem, it should be noted that the systems and techniques describedherein may be applied to any type of automation system. The energymanagement system 10 may include an energy data controller 12 that maybe used to perform various techniques described herein. As such, theenergy data controller 12 may include a processor 14, a memory 16, aninput/output (I/O) component 18, a communication component 20, and thelike. It should be noted that the energy data controller 12 may be anautomation controller, a personal computer, a programmable logiccontroller, an energy controller, a work station, a cloud-based system,or any computing device.

The processor 14 may be any type of computer processor or microprocessorcapable of executing computer-executable code. In certain embodiments,the processor 14 may include multiple cores such that each core may beused to perform different tasks. Additional details with regard to anembodiment of the processor 14 with multiple cores will be described ingreater detail below with reference to FIG. 17. The memory 16 may be anysuitable articles of manufacture that can serve as media to storeprocessor-executable code. These articles of manufacture may representcomputer-readable media (i.e., any suitable form of memory or storage)that may store the processor-executable code used by the processor 14 toperform the presently disclosed techniques. The I/O component 18 mayinclude one or more ports that may enable the energy data controller 12to connect to various types of computer media. The communicationcomponent 20 may be a wireless or wired communication component that mayfacilitate communication between various assets or controllers withinthe industrial automation system.

Referring back to the energy data controller 12, in certain embodiments,the energy data controller 12 may receive energy data 22, energystructure data 24, organizational model data 26, and asset profile data28. The energy data 22 may include energy information acquired byvarious assets within the industrial automation system. For instance,the energy data 22 may include voltage and power usage informationacquired from assets such as motor drives, variable frequency drives,soft starters, starters, power meters, motors, capacitor banks, aircompressors, refrigerator units, turbines, generators, energy storagedevices, photovoltaic cells, robots, reactors, and the like. In oneembodiment, the energy data 22 may include energy information acquiredfrom Common Industrial Protocol (CIP) energy objects disposed within theindustrial automation system. As such, the energy data 22 acquired byvarious assets in the industrial automation system may be communicatedor provided to the energy data controller 12.

In addition to the energy data 22, the energy data controller 12 mayreceive energy structure data 24 such as a one-line diagram, a powerschematic, or the like. That is, the energy structure data 24 mayinclude detail how power may be distributed to various assets in theindustrial automation system. For example, FIG. 2 depicts an example ofenergy structure data in the form of a power distribution schematic 40.Referring to FIG. 2, the power distribution schematic 40 may include amain power source 42, a number of transformers 44, a number of motors46, a number of capacitor banks 48, and a number of power meters 50.Generally, the power distribution schematic 40 illustrates how powerfrom the main power source 42 may be distributed to each asset in theindustrial automation system. However, although the power distributionschematic 40 may provide some details with regard to how energy may beconsumed within the industrial automation system, the power distributionschematic 40 provides no context in which the energy is consumed withrespect to how the industrial automation system is organized. In otherwords, the power distribution schematic 40 provides no information as towhich asset is in use with particular process and areas within theindustrial automation system.

Keeping this in mind, the energy data controller 12 may also receiveorganizational model data 26 that may indicate how the industrialautomation system is organized. That is, the organizational model data26 may provide a hierarchical structure of the automation systemrepresented in a functional view with respect to the industrialautomation system. As such, the organizational model data 26 may providelogical groupings of assets with respect to different areas (e.g.,cells, lines, sites, or enterprises/batches, continuous processes, ordiscrete manufacturing processes/infrastructure, manufacturing supportsystems, sub-assembly/batch systems, or core manufacturing systems) ofthe industrial automation system. For instance, in a broad sense, FIG. 3depicts an example of the organizational model data 26 that mayillustrate one embodiment of how the industrial automation system may beorganized. As such, the organizational model data 26 of FIG. 3 mayinclude a factory 60 that may encompass all of the industrial automationsystem. The factory 10 may be divided into a number of work areas 62,which may, for example, include different production processes that usedifferent types of assets. In one example, one work area may include asub-assembly production process and another work area may include a coreproduction process.

The work areas 62 may be subdivided into smaller units, work cells 64,which may be further subdivided into work units 68. Using the exampledescribed above, the sub-assembly production process work area may besubdivided into work cells 34 that may denote a particular team ofindividuals, a work shift time, or the like. These work cells 34 maythen be further subdivided into work units 68 that may includeindividual assets (e.g., motors, drives, compressors) that may be usedby the corresponding work cell 64. In certain embodiments, the factory60, the work areas 62, the work cells 64, and the work units 68 may becommunicatively coupled to a manufacturing support system 70, which mayreceive and monitor various data received from assets, controllers, andthe like in the factory 60, the work areas 62, the work cells 64, andthe work units 68. In addition to listing how the industrial automationsystem may be subdivided, the organizational model 26 may also detailhow each work area 62, work cell 64, and work unit 66 may interact witheach other. That is, each work area 62, for example, may be related to aparticular process of a manufacturing process. As such, theorganizational model 26 may detail which processes performed in certainwork areas 62 may depend on other processes being completed in otherwork areas 62.

The organizational model 26 may also include information related to howeach asset in the energy structure data 24 may relate to each area orsubarea of the industrial automation system. Moreover, theorganizational model 26 may include an energy profile for each assetused in the industrial automation system. The energy asset profile mayinclude any energy relevant information with regard to the correspondingasset. For instance, the energy asset profile may indicate an amount ofenergy consumed by the asset when operating at full load, an amount ofenergy consumed during start up, stop, and idle times, and the like. Theenergy asset profile may also include information related to how muchtime may be involved in starting an asset, how much time may be involvedin turning an asset off, how much time may be involved before an assetmay be fully functional after a light curtain has been broken, and thelike. The energy asset profile may be embedded within the asset itselfsuch that the asset may provide its corresponding energy asset profileas part of its corresponding energy data 22.

In one embodiment, the energy asset profile may indicate a unit orparameter of measurement (e.g., watts, joules) in which the asset mayprovide energy information. The organizational model data 26 may thusinclude context with regard to how each asset may operate with respectto its energy. Moreover, since the organizational model data 26 mayindicate a unit or parameter of measurement (e.g., watts, joules) foreach asset in the industrial automation system, the organizational modeldata 26 may be used to standardize the energy measurements for eachasset in the industrial automation system into a common unit.

Referring back to FIG. 1, if the organizational model data 26 does notinclude the energy asset profile for a particular asset, the energy datacontroller 12 may receive the corresponding asset profile via an assetprofile data 28. As such, the asset profile data 28 may be stored in adatabase or the like and may include the energy asset profile for anumber of assets that may be employed by the industrial automationsystem. In certain embodiments, the energy data controller 12 may querythe database to find the asset profile data 28 for a particular assetbased on the energy data 22, information from the energy structure data26, information from the organizational model data 26, or the like.

In any case, once the energy data controller 12 receive some or all ofthe energy data 22, the energy structure data 24, the organizationalmodel 26, and the asset profile data 28, the energy data controller 12may catalogue or categorize the energy data 22 with respect to theorganizational model. For example, the energy data controller 12 maycharacterize how the energy data 22 may relate to the organizationalmodel 26. FIG. 4 illustrates one example of how the energy data 22acquired by some of the assets depicted in the power distributionschematic 40 may relate to the example organizational model 26 depictedin FIG. 3. As such, assuming that the energy data 22 include energyinformation related to the motors 45 (M1, M2, M3, and M4), energyinformation related to the capacitor banks 48 (1 and 2), the energy datacontroller 12 may use the received organizational model 26 tocharacterize or denote that the energy data 22 related to motors M1 andM2 correspond to work area 1, the energy data 22 related to the motor M3and the capacitor bank 1 correspond to work area 2, and the motor M4 andthe capacitor bank 2 correspond to work area 3.

In addition to indicating which assets may be used in various work areas62, the organizational model 26 may indicate how each work area 62 maybe related to each other. For instance, work area 1 may correspond to apre-production process in a manufacturing process for a type of articleof manufacturing. After the article of manufacturing is pre-processed inwork area 1, the articles may be sent to work area 2 where the articlesare sub-assembled together. The articles may then be sent to work area3, which may include the core process for the manufacturing process.

By leveraging the energy data 22 with the organizational data 26, theenergy data controller 12 may determine efficient ways in which assetsin each work are 62 may operate. For example, if the assets in the workarea 3 are operating above its full capacity rating while the assets inwork areas 1 and 2 are each operating at 50% capacity, the energy datacontroller 12 may determine that an energy bottleneck may be present inwork area 3. As such, the energy data controller 12 may scale down theenergy consumption of the assets in work areas 1 and 2 such that theassets in work area 3 operate at its specified capacity. In this manner,the energy data controller 12 need not operate the assets in work areas1 and 2 inefficiently and may instead save the energy consumed in workareas 1 and 2 such that the assets in work area 3 may not be overloaded.

Moreover, by leveraging the energy data 22 with the organizational data26, the energy data controller 12 may categorize the received energydata 22 as physical energy data 30 or logical energy data 32 (i.e.,structured energy data). Physical energy data 30 may include energy datathat may be received directly from an asset and may indicate an amountof energy physically consumed by that particular asset. As such, thephysical energy data 20 may correspond to energy data received via powermeters 50 directly connected to the asset, CIP energy objects embeddedwithin the asset, or the like. By categorizing energy as physical energydata 30, the energy data controller 12 may enable operators to know howmany hours of operation that the asset may have been used, how muchenergy may have been consumed by the asset, and the like. As such, thisinformation may help maintenance personnel keep track of how each assetshould be maintained based on its actual usage data as opposed to randomschedule/calendar checks.

The energy data controller 12 may generate the logical energy data 32 byaggregating the energy data 22 based on the organizational model 26.That is, the energy data controller 12 may aggregate the energy data 22with respect to each area of the organizational model 26. In thismanner, the logical energy data 32 may depict the energy consumed ineach phase of the manufacturing process or in each area of theindustrial automation system. The logical energy data 32 may then beused to analyze how energy is being consumed within the industrialautomation system and between processes performed within the industrialautomation system.

In certain embodiments, the energy data controller 12 may calculateenergy data 22 for assets that may not provide energy data to the energydata controller 12. For instance, certain assets may not be equippedwith energy detection technology that may be used to determine an amountof energy being consumed by the asset. Alternatively, the asset may notinclude communication technology that may enable it to communicate itsenergy data 22 to the energy data controller 12. In this case, theenergy data controller 12 may use the energy data 22 received from knownassets in the industrial automation system and cross reference thatenergy data 22 with the energy structure data 24 and the organizationalmodel 26 to generate virtual energy data 34 (i.e., structured energydata) for the asset unable to provide energy information to the energydata controller 12. In one embodiment, the energy data controller 12 mayuse the energy structure data 24 to determine the relative position ofthe asset with respect to other assets that may have sent energy data22. Further, the energy data controller 12 may use the organizationalmodel data 26 to determine an energy profile for the respective asset.Using the relative position of the respective asset, the energy profilefor the respective asset, and the energy data 22 associated with theassets surrounding the respective asset, the energy data controller 12may predict energy (i.e., virtual energy data) being consumed by therespective asset.

In one embodiment, the energy data controller 12 may determine thevirtual energy data 34 of a first part of the industrial automationsystem based on energy data 22 received from assets that may representthe energy of a second part of the industrial automation system andenergy data 22 that may represent energy of both the first and secondparts of the industrial automation system. For example, referring toFIG. 3, the energy data controller 12 may determine the energy data 22related to the work area 1, which may be part of the first part of theindustrial automation system, based on the energy data 22 received froma power meters 50 that may represent the energy of the work areas 2 and3, which may be part of the second part of the industrial automationsystem, and the energy data 22 received from a power meter 50 thatrepresents the energy of all three work areas. That is, the energy datacontroller 12 may aggregate the energy data 22 representing the energyof work areas 2 and 3 and subtract the resulting aggregation from theenergy data representing the energy of all three work areas (1, 2, and3) to determine the virtual energy 34 that represent work area 1.

Keeping the foregoing in mind, the energy data controller 12 may use theenergy data 22, the energy structure 24, the organizational model 26,the asset profile data 28, the physical energy data 30, the logicalenergy data 32, the virtual energy data 34, and the like to generatevarious types of energy reports that may characterize how energy may beconsumed in the industrial automation system. FIGS. 5-7, for example,depict certain embodiments of different methods that may be employed bythe energy data controller 12 to generate various types of energyreports related to the industrial automation system.

Referring now to FIG. 5, the energy data controller 12 may generate anenergy report based on categories as determined using the organizationalmodel data 26 using method 70. Although the following description of themethod 70 is described as being performed in a particular order, itshould be noted that the method 70 is not restricted to the depictedorder and may instead be performed in some other orders.

At block 72, the energy data controller 12 may receive the energy data12 from assets that may be present in the industrial automation system.At block 74, the energy data controller 12 may receive theorganizational model data 26 related to the industrial automationsystem. In certain embodiments, the energy data controller 12 may alsoreceive the energy structure data 24 and the asset profile data 28 tosupplement the energy reports that may be generated by the energy datacontroller 12.

In any case, at block 76, the energy data controller 12 may categorizethe energy data 22 with respect to the organizational model data 26. Assuch, the energy data controller 12 may categorize the energy data 22based on particular areas in the organizational model 26 where theenergy data 22 may be located. In certain embodiments, the energy datacontroller 12 may also categorize the energy data 22 as physical energydata 30, logical energy data 32, or virtual energy data 34 based oninformation related to each asset as provided by the organizationalmodel data 26, as described above.

At block 78, the energy data controller 12 may generate energy reportsbased on how the energy data 22 was categorized in block 76. That is,the energy data controller 12 may generate energy reports that maydistinguish between physical energy data 30, logical energy data 32, andvirtual energy data 34. Moreover, the energy reports may aggregate theenergy data 22 according to particular work areas/cells/units in theindustrial automation system, as indicated by the organizational modeldata 26. In one embodiment, the generated reports may be depicted in anenergy dashboard or the like, which may display the energy properties ofvarious work areas, work cells, and work units in the industrialautomation system in real time.

By providing energy reports based on categorizations with respect to theorganizational model data 26, the energy data controller 12 may betterillustrate how individual work areas/cells/units consume energy and howthe energy consumed by the individual work areas/cells/units may berelated to other work areas/cells/units in the industrial automationsystem. Further, the energy reports may assist industrial automationsystem personnel allocate resources more efficiently based on how eacharea of the industrial automation system consumes energy with respect toproduction.

In certain embodiments, the organizational model data 26 may not includeenergy profiles for each asset provided in the organizational model data26 or for each asset that provided energy data 22. In this case, theenergy data controller 12 may provide a list of assets that may bemissing energy profiles to an operator or another controller that may beassociated with the industrial automation system. For instance, FIG. 6illustrates a method 90 that may be used to generate a list of assetsthat may not have energy profiles provided within the organizationalmodel data 26.

At block 92 and 94, the energy data controller 12 may receive energydata 22 and the organizational model data 26, respectively, as describedabove with respect to blocks 72 and 74 of FIG. 5. At block 96, theenergy data controller 12 may determine whether each asset providingenergy data 22 to the energy data controller 12 has a correspondingasset energy profile in the organizational model data 26. If the assetproviding the energy data 22 to the energy data controller 12 has acorresponding asset energy profile in the organizational model data 26,the energy data controller 12 may proceed to block 98 and block 100.That is, the energy data controller 12 may categorize the energy data 22with respect to the organizational model data 26 as described above withrespect to block 76 of FIG. 5. In the same manner, the energy datacontroller 12 may proceed to block 100 after the energy data 22 has beencategorized in block 98 as described above with respect to block 78 ofFIG. 5.

Referring back to block 96, if the asset providing the energy data 22 tothe energy data controller 12 does not have a corresponding asset energyprofile in the organizational model data 26, the energy data controller12 may proceed to block 102. At block 102, the energy data controller 12may generate a list of the missing asset energy profiles. In oneembodiment, the energy data controller 12 may then proceed to blocks 98and 100 to categorize the energy data 22 that have corresponding assetenergy profiles with respect to the organizational model data 26 andgenerate an energy report based on the categorizations. Here, the energyreport may include a disclaimer or note that indicates that all of theenergy data 22 received by the energy data controller 12 may not havebeen incorporated into the energy report. Alternatively, the energy datacontroller 12 may generate the energy report using only known energydata. As such, the report may include energy data for certain scaledareas within the organizational model data 26 if not all the energy data22 for the assets with the scaled areas are known.

In certain embodiments, instead of generating a list of missing assetenergy profiles, the energy data controller 12 may retrieve the missingasset energy profile from a database, the memory 16, or the like. Forexample, FIG. 7 depicts a method 110 that is similar to the method 90depicted in FIG. 6. As such, blocks 112-120 in FIG. 7 correspond toblocks 92-100 in FIG. 6. However, if, at block 116 of FIG. 7, the assetproviding the energy data 22 to the energy data controller 12 does nothave a corresponding asset energy profile in the organizational modeldata 26, the energy data controller 12 may proceed to block 122 andretrieve the missing asset energy profile. That is, the energy datacontroller 12 may receive information indicating a type of asset thatcorresponds to the received energy data 22. Using this information, theenergy data controller 12 may query a database, memory, or other digitalstorage unit to locate the asset energy profile that corresponds to themissing asset energy profile.

In one embodiment, the asset energy profile may be embedded within thecorresponding asset and the energy data controller 12 may query theasset for its energy asset profile. In another embodiment, the energydata controller 12 may request or receive an update that may include theasset energy profile for the asset.

After retrieving the missing asset energy profile, the energy datacontroller 12 may proceed to block 118 and generate energy reports basedthe energy data 22 received at block 112 with respect to theorganizational model data 26. Here, the energy data controller 12 mayinterpret the energy data 22 with respect to the asset energy profiledata 28 retrieved at block 122. As such, the energy data controller 12may interpret all of the energy data 22 in an appropriate context withrespect to the organizational model data 26. Alternatively, if theenergy data controller 12 did not receive the appropriate asset energyprofile, the energy data controller 12 may generate the energy reportsbased on aggregations or known data related to scaled areas in theindustrial automation system.

In addition to generating energy reports as described above, the energydata controller 12 may be used to control the operations of variousassets based on the energy data 22 with reference to the organizationalmodel data 26. As such, the energy data controller 12 may provide closedloop control for energy management of the industrial automation system.That is, the energy data controller 12 may monitor the energyconsumption and demand of the industrial automation system and adjust orcontrol the operations of various assets in the industrial automationsystem based on the demand.

Keeping this mind, FIG. 8 illustrates an aggregation system 130 thatuses an energy inference engine 132 to calculate confidence values forphysical energy data 30, logical energy data 32, and virtual energy data34. As such, the energy data controller 12 may determine how to controlthe various assets in the industrial automation system based on theconfidence values associated with the interpreted energy data.Generally, the inference engine 132 may receive the organizational modeldata 26, a metered energy asset data 134, derived energy asset data 136,unmetered energy asset data 138, a production or operational schedule145, or the like. The metered energy asset data 134 may include energydata that may be metered by a CIP energy object associated with an assetor the like. The metered energy asset data 134 may also be acquired fromvarious types of meters such as power meters, flow meters, and the like.In addition to the energy data that may be metered or measured, themetered energy asset data 134 may include a confidence value related tothe determined accuracy (e.g., +/−5%) of the metered or measured data.

The derived energy asset data 136 may also be provided by a CIP energyobject, which may have calculated or derived the derived energy assetdata 136 based on energy data received from other CIP energy objects andthe like. As such, the derived energy asset data 136 may include energydata for assets such as drives, overload pumps, fans, mixers, workcells, and the like. That is, the derived energy asset data 136 mayinclude energy data calculated for assets that do not have the abilityto measure its energy properties. Instead, the derived energy asset data136 may be calculated based on the asset energy profile for thecorresponding asset, as indicated in the organizational model data 26 orthe like, the energy structure data 24, and data that may representcertain electrical characteristics (e.g., voltage input, current output)related to the corresponding asset that may be used to derive energydata. For instance, if a drive is located between a transformer thatoutputs 500 volts and a motor that conducts 300 amps of current, thederived energy asset data 136 may derive the energy data thatcorresponds to the drive that may not provide energy data to the energyinference engine 132.

In one embodiment, if a drive is located between a transformer thatoutputs 500 watts of power and a motor that consumes 300 watts of power,and the asset energy profile 28 for the corresponding asset indicatesthat the asset consumes between 100 and 300 watts of power when inservice, the derived energy asset data 136 may denote that the driveconsumes 200 watts of power (virtual energy). In any case, the derivedenergy asset data 136 may be associated with a confidence value that maybe determined based on the confidence values related to the informationused to generate the derived energy asset data 136.

The unmetered energy asset data 138 may include other energy datareceived from various assets in the industrial automation system thatmay not have any metering capabilities such as motor starters, relays,lighting and the like. However, these assets may have fixed rate energyconsumption properties, which may be listed in its corresponding assetenergy profile data 28. As such, the unmetered energy asset data 138 mayinclude information that specifies a type of asset (e.g., product name,version, serial number) and its usage information. The usage informationmay relate to how many hours that the device has been in service or thelike. In this manner, the aggregator 144 may identify the asset energyprofile data 28 that corresponds to the asset providing the unmeteredenergy asset data 138 based on the information that specifies a type ofasset. The fixed rate energy data 142 may then be determined from theasset energy profile data 28 and may be used with the usage informationspecified by the unmetered energy asset data 138 to determine the energyconsumed by the unmetered asset. Like the metered energy asset data 134and the derived energy asset data 136 described above, the fixed rateenergy data 142 may also be associated with a confidence value that maybe denote the expected accuracy of the fixed rate energy data.

In one embodiment, an aggregator 144 may determine the fixed rate energydata as described above. Moreover, the aggregator 144 may receive theorganizational model data 26, the metered energy asset data 134, thederived energy asset data 136, the fixed rate data 142, and a productionor operational schedule 145. The aggregator 144 may then use theseinputs to determine updated confidence values for the metered energyasset data 134, the derived energy asset data 136, the fixed rate energydata 142, and the like. Using the received confidence values related tothe metered energy asset data 134, the derived energy asset data 136,the fixed rate energy data 142, along with the organizational model data26 and the operational schedule 145, the aggregator 144 may update theconfidence values for the metered energy asset data 134, the derivedenergy asset data 136, the fixed rate energy data 142 by checking afirst type of data associated with a first group of assets with respectto a second type of data associated with a second group of assets thatmay be related to the first group of assets according to theorganizational model data 26.

For example, referring to FIG. 4, the aggregator 144 may receive a firstderived energy asset data 136 from the motor M1. The first derivedenergy asset data 136 may include a first confidence value associatedwith the energy data in the first derived energy asset data 136. Theaggregator 144 may then receive a second derived energy asset data 136from the motor M2. The second derived energy asset data 136 may alsoinclude a second confidence value associated with the energy data in thesecond derived energy asset data 136. The aggregator 144 may thendetermine how the first derived energy asset data 136 and the secondderived energy asset data 136 may be associated with a logical grouping(e.g., work area, line, etc.) as per the organizational model data 26.Here, the aggregator 144 may determine that the first derived energyasset data 136 and the second derived energy asset data 136 may both beassociated with one particular logical grouping as per theorganizational model data 26. The aggregator 144 may then determine theenergy data associated with the identified logical grouping byaggregating the energy data in the first derived energy asset data 136and the second derived energy asset data 136. In addition, theaggregator 144 may also determine a confidence value for the aggregateddata (i.e., the energy data associated with the identified logicalgrouping) based on the confidence values associated with the firstderived energy asset data 136 and the second derived energy asset data136. As such, the aggregator 144 may determine the confidence value forthe aggregated data may be determined using various statisticaltechniques and the like.

Keeping this example in mind, the aggregator 144 update the confidencevalue for the aggregated data using additional energy data (e.g.,metered energy data) that may be associated with the energy of the samelogical grouping. For instance, referring to FIG. 4 again, if a powermeter was available measuring the energy related work area 1, theaggregator may use the metered energy asset data 134 from the powermeter to update the confidence value for the aggregated data. That is,the aggregator 144 may verify or check the derived asset energy data 136and its corresponding confidence value associated with the logicalgrouping against the metered energy asset data 134 and its correspondingconfidence value. As a result, the aggregator 144 may update theconfidence value of the derived asset energy data 136.

Keeping this example still in mind, the aggregator 144 may determine aconfidence value associated with virtual energy data 34. The is, theaggregator 144 may determine the confidence value associated a logicalgrouping according to the organizational model data 26 that theaggregator 144 may not have any energy data related thereto. Forinstance, if the aggregator received metered energy asset data 134 andits corresponding confidence value for all of the energy associated withboth work areas 1 and 2, the aggregator 144 may used the derived energyasset data 136 associated with work area 1 to verify the virtual energyasset data 138 associated with work area 2. As such, the aggregator 144may receive metered energy asset data 134 from a power meter or the likethat may measure the energy associated with work areas 1 and 2, whichmay both be part of a higher level (i.e., scaled) logical grouping ofthe industrial automation system according to the organizational modeldata 26. In one embodiment, the aggregator 144 may have determined thevirtual energy asset data 138 associated with the work area 2 becausethe energy data related to work area 2 may have been unknown to theaggregator 144. However, using the were part of a second logicalgrouping together according to the metered energy asset data 134 and itscorresponding confidence value for all of the energy associated withboth work areas 1 and 2 and the derived energy asset data 134 and itscorresponding confidence value for the energy associated with work area1, the aggregator 144 may verify the virtual energy asset data 138associated with the work area 2. Moreover, the aggregator 144 may updatethe original confidence value associated with the virtual energy assetdata 138 for work area 2 based on the confidence values for the derivedenergy asset data 136 associated with work area 1 and the metered energyasset data 134 associated with work areas 1 and 2.

In one embodiment, the aggregator 144 may also update the confidencevalue associated with the metered energy asset data 134, the derivedenergy asset data 136, the fixed rate energy asset data 142, or the likeusing operational status information related to the assets associatedwith the metered energy asset data 134, the derived energy asset data136, the fixed rate energy asset data 142, or the like. The operationalstatus information may include information detailing the currentoperating status of an asset (e.g., operating at full load, off) or thehistorical operating status of the asset (e.g., scheduled use of theasset over time). In certain embodiments, the operational statusinformation may be received via the production or operational schedule145.

Referring again to FIG. 4, the aggregator 144 may receive metered energyasset data 134 related to motor M1. The aggregator 144 may then use theoperational status history of M1 according to the production oroperational schedule 145 to verify the energy data provided by themetered energy asset data 134. As a result of the verification, theaggregator 144 may also update the confidence value associated with themetered energy asset data 134 related to motor M1. In one embodiment,the aggregator 144 may use the operational status in combination withthe corresponding asset profile data 28, which may be provided by theorganizational model data 26 or retrieved as described above.

In yet another embodiment, the aggregator 144 may verify the virtualenergy asset data 138 and its corresponding confidence value based onenergy data associated with a logical grouping of known energy data andthe virtual energy asset data 138. For example, referring again to FIG.4, the aggregator 144 may receive derived energy asset data 136 and itscorresponding confidence value associated with motor M1 and maydetermine virtual energy asset data 138 and its corresponding confidencevalue associated with motor M2. The aggregator 144 may also receivemetered energy asset data 134 and its corresponding confidence valueassociated with the work area 1, as defined according to theorganizational model data 22. Using the organizational model data 22,the aggregator 144 may determine that motor M1 and motor M2 may both bepresent in the work area 1. Moreover, using the operational status ofmotor M1 and motor M2 as received from the production or operationalschedule 145 along with the asset profile data 28 for the motor M1 andthe motor M2, the aggregator 144 may verify the accuracy of the virtualenergy asset data 138 for the motor M2, and thereby updating theconfidence value associated with the virtual energy asset data 138.

Although the examples described above have been made with specificreferences to specific types of data (i.e., metered, derived, orvirtual), it should be noted that the aggregator 144 may be used toupdate the confidence value of any type of energy data using the sameprocesses described above. Moreover, after updating the confidencevalues for any type of data, the energy data controller 12 may makeoperational decisions with respect to the industrial automation systemor any asset in the industrial automation system based on the updatedconfidence value. For example, the energy data controller 12 may send acommand to certain assets to perform an action when a certain confidencevalue is above or below a certain threshold.

Additionally, the aggregator 144 may determine physical energy data 30,logical energy data 32, or virtual energy data 34 (e.g., physical andlogical energy consumption 146) for various scalable parts of theindustrial automation system with respect to the organizational modeldata 26 based on the energy data that corresponds to the metered energyasset data 134, the derived energy asset data 136, the fixed rate energydata 142. The aggregator 144 may also determine system performancecalculations 148, which may be used to determine how the industrialautomation system may be performing. In particular, the systemperformance calculations 148 may indicate how each work area 62 or thelike in the industrial automation system may be operating in real time.The system performance calculations 148 may detail how the assets mayperform with respect to their energy data or the energy data of a groupin which they are part of with respect to the organizational model data22.

In addition to organizing the received energy data, the inference engine132 may output the metered data, derived data, fixed rate data, andvirtual data (i.e., load profiles) as localized reports 150, which maybe used to control the assets in the industrial automation system. Theinference engine 132 may also provide information related to variousareas of the industrial automation system or the industrial automationsystem as a whole to a scalable reporting component 152, which may usethe provided information to provide larger scale reports and the like.Further, the inference engine 132 may output all of its findings (e.g.,physical and logical energy consumption 146, system performancecalculations 148) to a database (e.g., historian 154) such that datarelated to the history of the industrial automation system is stored.

Keeping the foregoing in mind, the organizational model data 26 and theload profiles determined by the inference engine 132 may be used by anasset demand control system to control the operations of assets in theindustrial automation system based on demand data related to the assetsbeing used in the industrial automation system. For instance, FIG. 9illustrates an asset demand control system 160 that may use an energystate engine 162 to coordinate the use of assets in the industrialautomation system based on the energy demand on the assets. As such, inone embodiment, the energy data controller 12 may employ the assetdemand control system 160 to control the various assets in theindustrial automation system based on various energy demand provisions.

The energy state engine 162 may employ an operational demand managementcomponent 164, a demand control engine 166, and an asset schedulingcomponent 168 to control the assets in the industrial automation systembased on the corresponding energy demand on the assets. These componentsmay be used to coordinate the operations of assets that may becommunicatively coupled to assets 170 in the industrial automationsystem. The assets 170 may include any type of asset that may beemployed in the industrial automation system including various loads,machines, and the like. Further, the energy state engine 162 may controlthe manner in which the assets 170 may operate with respect to otherassets 170 in machine-to-machine relationships, other assets 170 in thesame work cell, other assets 170 in the facility, and the like. That is,the energy state engine 162 may coordinate the operations of the assets170 to manage the energy consumption and production along with thedemand of the assets by controlling the assets on an individual machinebasis, a machine-to-machine basis, a work cell basis, a facility basis,and the like.

In certain embodiments, the energy state engine 162 may receive inputssuch as organizational model and load profiles 172, system status 174,and policy 176. The organizational model and load profiles 172 mayinclude the organizational model data 26 and the load profiles asdetermined by the inference engine 132 described above. The systemstatus 174 may include information related to the status of theindustrial automation system, which may indicate that the operationalcapability of the industrial automation system, whether parts of theindustrial automation are not fully functional or fully staffed, or thelike. The policy 176 may denote an energy policy to govern theoperations of the industrial automation system. For instance, the policy176 may provide specifications that indicate that the industrialautomation system should operate at full capacity, in an energy savingsmode, operating only critical processes, and the like.

The energy state engine 162 may use the organizational model and loadprofiles 172, the system status 174, and the policy 176 inputs inaddition to inputs provided to the operational demand managementcomponent 164, the demand control engine 166, and the asset schedulingcomponent 168 to manage the use of the assets 170 with respect to theenergy consumed by the assets 170. Referring now to the operationaldemand management component 164, the operational demand managementcomponent 164 may analyze operational and non-operational events 178 andprovide information related to these events to the asset schedulingcomponent 168. The operational and non-operational events 178 mayinclude events when assets in the industrial automation system may beoperating and when they may not be operating. For instance, theoperational and non-operational events 178 may include times duringwhich corresponding assets are not operating due to scheduled breaks(e.g., lunch) for personnel operating the assets, shift changes for thepersonnel, product line changes, and the like. In one embodiment, theoperational and non-operational events 178 may be pre-defined accordingto a master schedule or the like. Alternatively, an operator may inputnew operational and non-operational events 178 such that the operationaldemand management component 164 may integrate the new operational andnon-operational events 178 into the existing operational andnon-operational events 178.

In any case, the operational demand management component 164 may providethe operational and non-operational events 178 to the asset schedulingcomponent 168 such that the asset scheduling component 168 mayincorporate the operational and non-operational events 178 into aschedule for each asset related to the operational and non-operationalevents 178. The asset scheduling component 168 may include a detailedschedule of how each asset in the industrial automation system will beused. For instance, the asset scheduling component 168 may include apredefined schedule for interlocking various assets, shedding the use ofvarious assets, and the like.

Moreover, the asset scheduling component 168 may dynamically adjust theschedule of how assets may be placed in service based on newly receivedinformation or data (e.g., operational and non-operational events 178).For example, the asset scheduling component 168 may dynamicallyinterlock assets or shed assets based on load profiles (i.e., receivedfrom inference engine 132), learned/adaptive energy pattern recognitionsof the assets, predictive energy models for the assets, newly discoveredassets, and the like. As such, the asset scheduling component 168 maydynamically adjust the schedule of how assets may be placed in serviceby modifying the current use of the assets to meet an energy policydefined by the policy 176 or the like.

The asset scheduling component 168 may also incorporate a rule basedschedule that indicates when the highest energy consuming assets may beplaced in service, when non-essential assets may be taken offline, andthe like. Moreover, the asset scheduling component 168 may also includesystem routines that may define how processes in the industrialautomation system may be performed. For instance, the system routinesmay indicate how the work areas 62 relate to each other with respect toa production process. As such, the scheduling component 168 may performsystem modulations such that different portions of the productionprocess may be performed at different times to accommodate variousenergy demands, policies, and the like.

The system routines may also include an energy exchange protocol thatmay enable the asset scheduling component 168 to exchange energy betweenassets. That is, the asset scheduling component 168 may use productionprocesses in the industrial automation system as a means to store energyor consume less energy. That is, the asset scheduling component 168 mayshift the production schedule and adjust the scheduled use of the assets170 to conserve energy when bottlenecks in production or energy havebeen identified. In this manner, the asset scheduling component 168 mayuse work-in-progress (WIP) processes as a battery to store energy as abattery. As a result, the asset scheduling component 168 may enable theindustrial automation system to operate more energy efficiently withoutsacrificing production productivity.

In one embodiment, the asset scheduling component 168 may recognizeenergy patterns based on the organizational model and the load profiles172. That is, the asset scheduling component 168 may analyze the loadprofiles for each asset over time and leverage the load profiles withthe organizational model data 26 to identify patterns of energy use ormodel the energy of the industrial automation system within differentwork areas 62, work cells 64, and work units 66 of the industrialautomation system. After identifying the energy patterns of the assets170, the asset scheduling component 168 may dynamically adjust theschedule of how assets may be placed in service such that the energypatterns by the assets 170 may meet energy patterns specified by thepolicy 176 or the like.

In addition to or in lieu of specifying energy patterns for the assets170, the policy 176 may specify the energy demand schedule of the assets170 of the industrial automation system. The energy demand schedule ofthe assets 170 may include a schedule that specifies the amount ofenergy demanded by the assets 170 over time. As such, the energy stateengine 162 may control the use of the assets 170 to meet the energydemand schedule specified by the policy 176.

To manage and control the operations of the assets 170 with respect toenergy demands, the demand control engine 166 may provide various typesof energy demand information to the asset scheduling component 168 suchthat the asset scheduling component 168 may schedule the use of theassets 170 based on the energy demand information. Generally, the energydemand information may provide guidelines in which the assets 170 in theindustrial automation system should operate under various energy demandscenarios. For instance, the demand control engine 166 may include anenergy demand management plan that may detail a schedule of energydemand for each asset 170 over time.

The demand control engine 166 may also include an energy demand responseplan that may provide provisions to shed energy consumptions when energydemands of the assets 170 exceed some threshold. In the same manner, theenergy demand response plan may also provide provisions to provideregenerative energy back to the utility when energy demands of theassets 170 are below some threshold.

The demand control engine 166 may also include a dynamic energy pricemanagement plan that may provide guidelines to operate the assets 170based on dynamic energy pricing. For example, energy usage in certainhours of the day, month, or year may have higher utility costs asopposed to other hours. As such, the dynamic energy price managementplan may specify how the assets 170 should be scheduled in accordancewith the dynamic energy prices such that the industrial automationsystem efficiently meets its production goals while minimizing energycosts.

In determining the demand management plan, the demand response plan, thedynamic price management plan, the demand control engine 166 may usecertain inputs such as energy event 180, system event 182, energyvariables 184, and business rules 186. The energy event 180 may includeinformation related to an energy demand such as a large energy demand asindicated by the utility or the like. That is, the utility may provideinformation indicating that the utility may experience a large energydemand during certain hours of a day (e.g., hours when temperatures areexpected to be extremely high). As such, the demand control engine 166may determine how to reduce the energy demand of the assets 170 duringthe hours that the utility may experience the large energy demand. Inone embodiment, the demand control engine 166 may provide energy demandinformation or asset use determinations to the asset schedulingcomponent 168, and the asset scheduling component 168 may modify the useof assets 170 based on the energy demand information or the asset usedeterminations.

In another embodiment, the demand control engine 166 may negotiate orcommunicate b-directionally with the provider of the energy event 180 todetermine a demand management plant to meet the energy event 180. Assuch, the demand control engine 166 may send a request to the assetscheduling component 166 or the like to modify the operations of theassets 170 to meet the demand according to the energy event 180. In oneembodiment, the demand control engine 180 may request that the assetscheduling component 166 or the like may cause the assets 170 to produceenergy to provide back to the grid.

When negotiating, the demand control engine 168 may provide informationto the utility or the like related to the energy capabilities of theassets 170 or the energy data related to parts of the industrialautomation system according to the organizational model data 172. Theutility and the demand control engine 164 may thus negotiate together todetermine a way to minimize an adverse impact of the energy event 180based on the capabilities of the assets 170 and the utility.

The system event 182 may include information related to an event induring the production process in the industrial automation system thatmay demand a higher than expected amount of energy. For instance,production in the industrial automation system may be increased to meetvarious production goals or the like. In this case, the demand controlengine 166 may specify to the asset scheduling component 168 that theenergy demand of the assets 170 will increase above expected levels dueto the information provided in the system event 182. The assetscheduling component 168 may then, in turn, modify the scheduled use ofthe assets 172 to meet the energy demand details as provided by thedemand control engine 184. Like the energy event 180, the demand controlengine 168 may negotiate with the provided of the system event 182 todetermine an efficient way to resolve the system event 182 together, asdescribed above.

Another input that may modify the energy demand schedule of the assets170 may include the energy variables 184. The energy variables 184 mayinclude information related to a dynamic price schedule for the use ofenergy from the utility, a restriction on the use of a certain amount ofenergy from the utility, or the like. Here, the demand control engine166 may determine how to minimize the energy consumption of the assets170 based on the dynamic price schedule. The asset scheduling component168 may then modify the scheduled use of the assets 172 to meet theenergy demand details as provided by the demand control engine 184.

Yet another input that may be used by the demand control engine 166 mayinclude the business rules 186. The business rules 186 may detail howvarious energy demand scenarios may be handled by the demand controlengine 166. As such, the business rules 186 may include any type ofenergy demand rule as specified by an operator of the industrialautomation system. For example, the business rules 186 may includeproviding an average overall energy consumption value for the industrialautomation system over a specified amount of time. As such, the demandcontrol engine may determine how energy demands of the assets 170 relateto the average overall energy value and may specify to the assetscheduling component 168 to increase or decrease the energy of theassets 170 to meet the average overall energy value. In anotherembodiment, the business rules may provide a depreciation schedule foreach asset 170. As such, the demand control engine 168 may send commandsto the assets scheduling component 166 to operate the assets 170according to the depreciation schedule or based thereon.

As mentioned above, the demand control engine 166 may communicate withthe asset scheduling component 168 to control the energy of the assets170. As such, the asset scheduling component 168, in certainembodiments, may be communicatively coupled to the assets 170 such thatthe energy or the use of the assets 170 may be controlled directly bythe asset scheduling component 168. Generally, however, a program 188may control the operations of the assets 170.

The program 188 may be a program that may provide a user interface orthe like to operate one or more of the assets 170. In certainembodiments, one program 188 may be associated with each type of asset170 (e.g., drives, motors). In other embodiments, one program 188 mayinterface with multiple types of assets and thus may be used to controlthe multiple types of assets. As such, an operator of the industrialautomation system may program or control the operations of the assets170 using the program 188.

In the asset demand control system 160, however, the program 188 mayinterface with the energy state system 162. More specifically, variouscomponents within the program 188 may interface with the operationaldemand management component 164, the demand control engine 166, and theasset scheduling component 168.

Keeping this in mind, the program 188 may include a program statuscomponent 190, program sub-routines component 192, program configuredload profile component 194, and program demand control (DC) routinescomponent 196. The program status component 190 may indicate the statusof the program such as whether the program is active, operational, andthe like. In one embodiment, the program status component 190 mayindicate the current state of one or more of the assets 170 or thecurrent state of each of the components within the program 190. As such,the energy state engine 162 may know the current operations of eachasset 170 or the program 188.

The program sub-routines component 192 may include computer-executableinstructions or subroutines that may be defined to support when theassets 170 may schedule breaks (e.g., lunch), end of shifts, a productline change over, and the like. As such, in one embodiment, theoperational demand management component 164 may interface directly withthe program sub-routines component 190 to incorporate theoperational/non-operational event data 178. The program sub-routinescomponent 192 may then implement the changes to the scheduled control ofthe assets 170 based on the information provided by the operationaldemand management component 164.

The program configured load profile component 194 may indicate how eachload or asset 170 may be configured. That is, the program configuredload profile component 194 may indicate which of the assets 194 may bedeemed critical or non-critical to certain production processes beingperformed in the industrial automation system. Moreover, the programconfigured load profile component 194 may also indicate which of theassets 170 include safety interlocks, are associated with certainuser-defined restrictions, and similar types of information that may bespecific to a particular asset 170. As such, the information containedin the program configured load profile component 194 may be provided tothe asset scheduling component 168 such that the asset schedulingcomponent 168 may be aware of the various operating characteristics ofeach asset 170. The asset scheduling component 168 may then coordinatethe operations of the assets 170 in accordance with the informationprovided by the program configured load profile 194.

The program demand control routines component 196 may includecomputer-executable instructions to control the operations of the assets170 based on various energy demand characteristics. For instance, theprogram demand control routines component 196 may provide procedures inwhich to operate the assets 170 based on energy demand parametersprovided by the demand control engine 166. The program demand controlroutines component 196 may control the energy demands of the assets 170using a number of techniques. In one example, the program demand controlroutines component 196 may implement a program modulation which maycause the assets 170 in each work area 62, work cell 64, or work unit 62to modulate their energy according to a specified pattern or such thatthe overall energy demand of the industrial automation system isreduced.

The program demand control routines component 196 may also employ anenergy exchange technique in regulating the energy demand of the assets170. The energy exchange technique may involve transferring storedenergy between assets 170, work areas 62, work cells 64, work units 66,and the like such that the overall energy demand of the industrialautomation system is reduced, matches a specified pattern, or the like.The energy exchange technique may also alter the manner in which aproduction process may be performed in order to reduce the overallenergy demand of the industrial automation system for different periodsof time. In certain embodiments, the energy exchange technique mayinvolve determining how each asset 170 may operate more efficientlybased on information stored in the corresponding asset energy profiledata 28. As such, the program demand control routines component 196 maythen directly configure the corresponding asset 170 to operate moreefficiently by ensuring that the asset 170 is operating as per theinformation indicated in the corresponding asset energy profile data 28.

Other methods in which the program demand control routines component 196may control the demand of the assets 170 may include sending commands tocertain assets 170 to generate energy for the industrial automationsystem or release stored energy in various batteries, capacitor banks,and the like. The program demand control routines component 196 may alsostagger loads such that multiple loads or machines may be operating atdifferent times in order to reduce the overall energy demand of theindustrial automation system. The program demand control routinescomponent 196 may also send commands to assets 170 that haveregenerative loads to redirect the regenerative energy back to theindustrial automation system, the utility (e.g., grid), or the like.

Keeping the foregoing in mind, FIGS. 10-14 depict flow charts of variousmethods that may be employed in managing the energy properties of theassets 170 of the industrial automation system based on informationgathered from the organizational model data 22, the energy state engine162, and the like. Referring now to FIG. 10, FIG. 10 depicts a flowchart of a method 220 for placing assets 170 in the industrialautomation system into a reduced power consumption mode based on energyinformation determined by the energy inference engine 132, the energystate engine 162, or via the organizational model data 26. In certainembodiments, the method 220 may be performed by the energy datacontroller 12, which may be communicatively coupled to the assets 170.

At block 222, the energy data controller 12 may receive structuredenergy data related to an industrial automation system. The structuredenergy data may depict the energy data 22 with respect to theorganizational model data 26 as described above. As such, the structuredenergy data may include the energy data 22 organized as physical energydata 30, logical energy data 32, and virtual energy data 34.

Using the structured energy data related to the industrial automationsystem, the energy data controller 12 may, at block 224, determine anamount of energy currently being consumed by each work area 62, workcell 64, work unit 62, and the like in the industrial automation system.The energy data controller 12 may also determine an amount of energybeing consumed by each asset 170.

At block 226, the energy data controller 12 may identify any work area62, work cell 64, work unit 62, and the like or any asset 170 that maybe idle. That is, the energy data controller 12 may analyze the currentamounts of energy being consumed according to the structured energy datato determine which parts or assets in the industrial automation systemare not currently in service or use.

At block 228, the energy data controller 12 may send commands toindividual idle assets or assets in parts of the industrial automationsystem identified as being idle to enter into a reduced energy mode ofoperation. As such, the idle assets may not waste energy byunnecessarily keeping the entire asset powered on. Instead, the reducedenergy mode may enable the idle assets to keep critical operationsrunning while minimizing the use of non-critical operations. In oneembodiment, the reduced energy mode may involve placing the asset 170offline such that it consumes no energy. This case may be limited,however, to those assets that may be quickly brought back online withoutinvolving a long start-up or warm-up process.

In certain embodiments, the energy data controller 12 may, at block 224,determine how each part of the industrial automation system and eachasset 170 in the industrial automation system may consume energy withrespect to time. At block 226, the energy data controller 12 mayrecognize periods of time or patterns of energy use when parts of theindustrial automation system or the assets 170 may be idle for someperiod of time. Here, the energy data controller 12 may, at block 228,send commands to the asset scheduling component 168 to modify theoperations of the assets in the identified areas of the industrialautomation system at similar periods of time or based on the patterns ofenergy use. That is, the asset scheduling component 168 may adjust thescheduled use of the identified assets such that they enter the reducedenergy mode during time periods when the assets or the parts of theindustrial automation system are expected to be idle.

FIG. 11 depicts a flow chart of a method 240 for coordinating the use ofthe assets 170 in the industrial automation system based on peak energytimes and energy information determined by the energy inference engine132, the energy state engine 162, or the organizational model data 26.Like the method 220 of FIG. 10, in certain embodiments, the method 240may be performed by the energy data controller 12, which may becommunicatively coupled to the assets 170.

At block 242 and block 244, the energy data controller 12 may receivethe structured energy data and may determine the energy for each asset170 and part of the industrial automation system over time as describedabove with respect to blocks 222 and 224 of FIG. 10. At block 246, theenergy data controller 12 may identify periods of time when theindustrial automation system may have its highest energy demands (i.e.,peak demand times).

At block 246, the energy data controller 12 may coordinate theoperations of the assets 170 with respect to the organizational modeldata 26 such that the peak energy demand of the industrial automationsystem may be reduced. As such, the energy data controller 12 may usethe demand control engine 166 and/or the asset scheduling component 168to coordinate the operations of the assets 170 such that the assets 170use less energy as described above. For instance, the energy datacontroller 12 may use the assets 170 such that they stagger loads.Alternatively or additionally, the energy data controller 12 may sendcommands to assets 170 capable of generating energy (e.g., generators)to generate energy for the industrial automation system such that thenet result is that the overall energy use of the industrial automationsystem is reduced.

In any case, by reducing the peak energy demand of parts of theindustrial automation system or assets 170 in the industrial automationsystem, the energy data controller 12 may enable the industrialautomation system to operate more efficiently. Moreover, by reducing thepeak energy demand of the industrial automation system, the energy datacontroller 12 may reduce the stress that may be placed on the utilityproviding the energy or the assets 170 consuming the energy. As aresult, the industrial automation system may be more sustainable and therisk of failures occurring within the industrial automation system dueto situations when too much energy is being consumed may be averted.

FIG. 12 depicts a flow chart of a method 260 for coordinating the use ofthe assets 170 in the industrial automation system based on a utilitydemand schedule and energy information determined by the energyinference engine 132, the energy state engine 162, or the organizationalmodel data 26. Like the method 220 of FIG. 10, in certain embodiments,the method 260 may be performed by the energy data controller 12, whichmay be communicatively coupled to the assets 170.

At block 262, the energy data controller 12 may receive the structuredenergy data for each asset 170 and each part of the industrialautomation system as described above with respect to block 222 of FIG.10. At block 264, the energy data controller 12 may receive utilitydemand data from a utility, an energy provider, or the like. The utilityenergy demand data may include information related to time periods thatthe utility may experience peak demand, rates for energy consumption atdifferent time periods, and the like. In one embodiment, the utilityenergy demand data may include a request to reduce energy consumptionduring certain hours, a request to provide energy back to the gridduring certain hours, or the like.

At block 266, the energy data controller 12 may determine how theoperations of the assets 170 may be adjusted to accommodate the utilityenergy demand data based on the structured energy data. That is, theenergy data controller 12 may predict an amount of energy in parts ofthe industrial automation system or the entire industrial automationsystem may be consumed over time. The energy data controller 12 may thendevelop a strategy that may accommodate at least some of the utilityenergy data based on these predictions.

At block 268, the energy data controller 12 may send commands to modifythe operations of the assets 170 based on the utility energy demanddata. That is, the energy data controller 12 may interface with thedemand control engine 166, the asset scheduling component 168, or thelike to reduce energy consumption by parts of the industrial automationsystem or the entire industrial automation system based during peakdemand for the utility.

The energy data controller 12 may also modify the scheduled use of theassets 170 such that the energy consumed by parts or the entireindustrial automation system is the most economical based on the pricingor rate schedule for energy consumption as provided by the utilityenergy demand data. For example, the energy data controller 12 may shiftsome of the core processes of the industrial automation system to beperformed during off-peak (i.e., low rate) hours such that theindustrial automation system may reduce its costs with regard to theenergy it consumes.

In one embodiment, the energy data controller 12 may send commands tothe assets 170 (via the demand control engine 166, the asset schedulingcomponent 168, or the like) to feed or supply energy back to the grid asper request indicated in the utility energy demand data. As such, theenergy data controller 12 may instruct the assets 170 capable ofgenerating energy to generate energy and direct the energy to theutility grid. Similarly, the energy data controller 12 may instruct theassets 170 having regenerative energy characteristics to direct theregenerative energy to the utility grid.

In yet another embodiment, the energy data controller 12 may sendcommands to the assets 170 (via the demand control engine 166, the assetscheduling component 168, or the like) to improve the power quality ofthe industrial automation system. As such, the energy data controller 12may instruct the assets 170 having inductive loads or potentiallyaffecting the power quality of the industrial automation system to powerdown.

FIG. 13 depicts a flow chart of a method 270 for notifying an operatorin the industrial automation system when energy usage of a componentfalls outside an expected range based on energy information determinedby the energy inference engine 132, the energy state engine 162, or theorganizational model data 26. Like the method 220 of FIG. 10, in certainembodiments, the method 270 may be performed by the energy datacontroller 12, which may be communicatively coupled to the assets 170.

At block 272, the energy data controller 12 may receive the structuredenergy data for each asset 170 and each part of the industrialautomation system as described above with respect to block 222 of FIG.10. At block 274, the energy data controller 12 may determine anexpected range of energy values for each part of the industrialautomation system, the entire industrial automation system, each asset170, and the like based on the structured energy data over time. Thatis, the energy data controller 12 may monitor and record the energypattern of each part of the industrial automation system, the entireindustrial automation system, each asset 170, and the like over someperiod of time. The energy data controller 12 may then determine a rangeof expected energy values for various segments of time during the periodof time based on the recorded energy values. In one embodiment, therange of expected energy values may include energy data that have beenattributed to valid or normal energy values for the respective part ofthe industrial automation system, the entire industrial automationsystem, each asset 170, or the like. That is, the recorded energy valuesthat may be attributed to adverse or irregular circumstances (e.g.,fault) may be removed from consideration as part of the range ofexpected energy values.

At block 276, the energy data controller 12 may receive energy data 22in real time from the assets 170. As such, the energy data controller 12may receive energy figures from power meters 50 coupled to the assets170, directly from the assets 170 (e.g., CIP energy objects), or thelike. In certain embodiments, the energy data controller 12 maydetermine the physical energy data 32, the logical energy data 34, andthe virtual energy data 36 that correspond to the current state of partsof the industrial automation system, the entire industrial automationsystem, the assets 170, and the like based on the structured energydata.

At block 278, the energy data controller 12 may determine whether thereal-time energy data received at block 276 falls within the range ofexpected energy values. As such, the energy data controller 12 maydetermine whether various scales (e.g., work area, work cell, work unit,asset) of the real-time energy falls within the corresponding scaledrange of expected energy values. If the real-time energy data does notfall within the range of expected energy values, the energy datacontroller 12 may proceed to block 280 and send a notification to asupervisory controller, an operator of the industrial automation system,or the like. In this way, the operator may be aware of any problems orpotential problems that may be occurring in the industrial automationsystem based on the energy being consumed by the industrial automationsystem.

If, however, at block 278, the real-time energy data does indeed fallwithin the range of expected energy values, the energy data controller12 may return to block 276 and send continue to receive energy data 22in real time. The method 270 may thus run continuously such that theenergy properties of the industrial automation system are continuouslymonitored.

FIG. 14 depicts a flow chart of a method 290 for modifying a scheduleduse of the assets 170 in the industrial automation system based on autility demand schedule and energy information determined by the energyinference engine 132, the energy state engine 162, or the organizationalmodel data 26. Like the method 220 of FIG. 10, in certain embodiments,the method 290 may be performed by the energy data controller 12, whichmay be communicatively coupled to the assets 170.

At block 292, the energy data controller 12 may receive the structuredenergy data for each asset 170 and each part of the industrialautomation system as described above with respect to block 222 of FIG.10. At block 294, the energy data controller 12 may receive an assetschedule that may indicate how the assets 170 are scheduled for use withrespect to the organizational model data 26.

The energy data controller 12 may then, at block 296, determine theenergy of parts of the industrial automation drive, the entireindustrial automation drive, the assets 170, or the like based on thestructured energy data and the asset schedule. That is, the energy datacontroller 12 may calculate or predict the amount of energy each asset170 may consume or produce if each asset 170 is operated according tothe asset schedule and exhibit energy properties as specified in thestructured energy data.

At block 298, the energy data controller 12 may modify the scheduled useof the assets 170 (i.e., the asset schedule) such that the energyconsumption does not exceed some energy consumption target. The energyconsumption target may specify an amount of energy that may be consumedby the assets 170 in scalable terms with respect to the industrialautomation system. For example, the energy consumption target mayprovide an energy consumption value for multiple work areas 62 in theindustrial automation system, the factory 60, or the like.

When modifying the scheduled use of the assets 170, the energy datacontroller 12 may adjust the use of the assets 170 as described above.In certain embodiments, the energy data controller 12 may adjust theasset schedule such that the level of productivity of the industrialautomation system may be maintained while operating more efficientlywith respect to energy being consumed by the industrial automationsystem. After modifying the asset schedule, the energy data controller12 may predict whether the energy consumption of the assets 170 will bebelow the energy set point. If not, the energy data controller 12 mayiteratively adjust the asset schedule and predict the energy consumptionof the assets 170 based on the adjusted asset schedule until the energyconsumption of the assets 170 is below the energy target.

FIG. 15 is a block diagram of a multi-core processor 300 that may beemployed in the energy management system 10. As shown in FIG. 15, theprocessor 14 of the energy data controller 12 may include multipleindependent central processing units (CPUs) or cores. In one embodiment,the processor 14 may include four cores as shown in FIG. 15; however, itshould be noted that the processor 14 may include any number of cores.By using multiple cores in the processor 14, computing operations fordifferent functions may be performed by different cores. As a result,the processor 14 may perform different functions in parallel, therebyperforming each function more quickly.

In one embodiment, the processor 14 may include an energy core 302, acontrol core 304, a security core 306, and a safety core 308. The energycore 302 may perform energy data interpretations such as leveraging theenergy data 22 with the organizational model 26, as described above withrespect to FIGS. 1-14. As a result, the energy core 302 may continuouslycalculate the physical energy data 30, the logical energy data 32, andthe virtual energy data 34 (i.e., structured energy data) in real time.Moreover, the energy core 302 may monitor power quality of parts of theindustrial automation system, the entire industrial automation system,or the like. As such, the energy data core 302 may monitor an electronicsignature of parts of the industrial automation system, the entireindustrial automation system, or the like to predict a peak energydemand. In this case, the energy core 302 may send commands to thecontrol core 304 to adjust the operations of the assets 170 to preventreaching the peak energy demand or the like.

The control core 304 may, in one embodiment, perform control relatedfunctions for the assets 170 based on the structured energy data or thelike as described above with respect to FIGS. 8-14. That is, theprocesses and functionalities of the inference engine 132 and the energystate engine 162 may continuously be performed within the energy core302, which may send control commands to the control core 304. As aresult, the energy data controller 12 may control the assets 170 in realtime using the control core 304 based on real time energy datadetermined by the energy core 302. Moreover, since the processes of theenergy core 302 and the control core 304 may be performed in parallel,the energy data controller 12 may respond more quickly and control theoperations of the assets 170, the assets 170 within a part of theindustrial automation system, or the assets 170 in the entire industrialautomation system based on real time energy data related to the same.

In addition to the energy core 302 and the control core 304, theprocessor 14 may use the security core 306 and the safety core 308 tomonitor and control the security and safety operations of the industrialautomation system. For instance, the security core 306 may monitorvarious security signals received from devices intended to protect theindustrial automation system from unauthorized use.

Similarly, the safety core 308 may monitor the safety devices in theindustrial automation system and send notifications to certain personnelwhen the safety of operators in the industrial automation system isbeing compromised. For example, the safety core 308 may monitor datareceived from light curtains designed to ensure that humans do not entera particular area. If however, the safety core 308 receives a signalfrom a light curtain indicating that the light curtain may have beenbroken, the safety core 308 may send commands to the assets 170 locatedwithin the light curtain to power down. The safety core 308 may alsosend a notification to an appropriate party indicating that the lightcurtain was broken. In one embodiment, the safety core 308 may use thestructured energy data from the energy data core to determine the assets170 that may be located within the light curtain and may send commandsto the devices providing energy to those assets 170 to power down,thereby effectively powering down the assets 170 by isolating the assets170 from its power source.

In certain embodiments, the energy data controller 12 may work inconjunction with cloud-based systems and the like to perform large datacomputations related to the processes described above and the like.

For example, in one embodiment a method in a computing system forperforming statistical computations on a data set that is larger thancan fit in memory practicably and using said data set for controllingassets 170 in the industrial automation system based on certain energymanagement criteria may include providing the data set that includeenergy information collected from a plurality of assets. Each of theassets may operatively communicate to an external data storage medium(e.g., cloud-based system) and utilize an energy object having anidentifier associated with an energy resource and a measurementcharacteristic associated with the energy resource. The method may theninclude performing a statistical computation on the data by accessingand processing the data at the external data storage medium andcommunicating information back to a controller (e.g., energy datacontroller 12) associated with the asset for performing energymanagement actions.

While only certain features of the invention have been illustrated anddescribed herein, many modifications and changes will occur to thoseskilled in the art. It is, therefore, to be understood that the appendedclaims are intended to cover all such modifications and changes as fallwithin the true spirit of the invention.

The invention claimed is:
 1. A system, comprising: a processorconfigured to: receive energy data associated with one or more assets inan automation system; receive organizational model data associated withthe automation system, wherein the organizational model data comprisesan asset energy profile for at least one of the assets; generate a listof missing assets that do not have an asset energy profile in theorganizational model data; and generate one or more energy reports basedon a relationship between a first subset of the energy data, a secondsubset of the energy data, a third subset of the energy data, and theorganizational model data, wherein the first subset of the energy datacorresponds to physical energy data received directly from the at leastone of the assets, wherein the second subset of the energy datacorresponds to virtual energy data corresponding to at least one missingasset of the list of missing assets, and wherein the third subset of theenergy data corresponds to an aggregation of at least a portion of thephysical energy data and a portion of the virtual energy data aggregatedwith respect to the organizational model data control the assets basedon the received energy data and the received organizational model. 2.The system of claim 1, wherein the energy data is received from theassets.
 3. The system of claim 1, wherein the assets comprise one ormore power meters, one or more drives, one or more motors, one or morecapacitor banks, one or more air compressors, one or more refrigeratorunits, one or more turbines, one or more generators, one or more energystorage devices, one or more photovoltaic cells, one or more robots, oneor more reactors, or any combinations thereof.
 4. The system of claim 1,wherein the organizational model data comprises one or more work areas,one or more work cells, one or more work units, or any combinationthereof.
 5. The system of claim 1, wherein the organizational model datacomprises one or more cells, one or more lines, one or more sites, oneor more enterprises, or any combination thereof.
 6. The system of claim1, wherein the organizational model data comprises infrastructure, oneor more manufacturing support systems, one or more sub-assembly/batchsystems, one or more core manufacturing systems, or any combinationthereof.
 7. The system of claim 1, wherein the organizational model datacomprises one or more energy profiles that correspond to the assets,wherein the energy profiles define energy consumption characteristics ofthe assets or energy production characteristics of the assets.
 8. Thesystem of claim 1, wherein the physical energy data comprises meteredenergy data, derived energy data, fixed energy data, or any combinationthereof.
 9. The system of claim 1, wherein the energy reports compriseone or more associations between the assets and one or more groupswithin the organizational model data.
 10. The system of claim 1, whereinthe organizational model data comprises associations between the one ormore assets and one or more logical groupings that correspond to one ormore functionalities of the automation system.
 11. A system, comprising:a processor configured to: receive energy data associated with one ormore assets in an automation system; receive organizational model dataassociated with the automation system, wherein the organizational modeldata comprises an asset energy profile for at least one of the assets,wherein the asset energy profile comprises energy consumption propertiesof the at least one of the assets with respect to different operatingconditions for the at least one of the assets; retrieve one or moremissing asset energy profiles that are not in the organizational modeldata; categorize the energy data, wherein a first portion of the energydata is categorized as physical energy data directly received from theassets, a second portion of the energy data is categorized as virtualenergy data corresponding to at least one retrieved missing asset energyprofile, and wherein a third portion of the energy data is categorizedas logical energy data corresponding to an aggregation of at least aportion of the physical energy data and a portion of the virtual energydata aggregated with respect to the organizational model data; andgenerate one or more energy reports based on a relationship between thephysical energy data, the virtual energy data, and the logical energydata control the assets based on the received energy data and thereceived organizational model.
 12. The system of claim 11, wherein theprocessor is configured to retrieve the missing asset energy profiles bydownloading the missing asset energy profiles from a database.
 13. Thesystem of claim 11, wherein the processor is configured to retrieve themissing asset energy profiles by querying corresponding assets for themissing asset energy profiles, wherein the missing asset energy profilesare embedded within the corresponding assets.